THE EFFECT OF EARNINGS SMOOTHING, INVESTMENT OPPORTUNITIES, AND RETURN OF ASSET ON
EARNINGS AGGRESIVENESS
(Empirical Study from Manufacturing Company in Indonesia)
Melinda Malau
Universitas Kristen Indonesia, Jakarta Jl. Mayjend Sutoyo No.2 Jakarta Timur, Indonesia
[email protected] ABSTRACT
This study aims to examine and analyze whether earnings smoothing, investment, and return on asset affect the earnings aggresiveness. The method is panel regression analysis.
The sample used 500 observations in Indonesia from manufacturing companies for the period 2013-2017. The results show that earnings smoothing has a significant positive effect on the earnings aggressiveness. Return on asset has a significant negative effect on earnings aggressiveness. This research gives theoretical implications that earnings smoothing and return on asset have a significant effect on the earnings aggressiveness. It has managerial implication for regulators. The formulation of regulations in a country's Financial Accounting Standards is applied as form of limiting the flexibility of accounting policies and to narrow the opportunist attitudes of management.
Keywords: earnings aggresiveness, earnings smoothing, investment, return on asset.
INTRODUCTION
Companies that are already listed on the capital market must pay attention to capital costs because the calculations are used to produce the right funding decisions (Lambert et al., 2007). Funding undertaken should provide results that can improve the welfare of stakeholders. Decisions that are often faced by financial managers in the company's operational activities are capital structure decisions, namely financial decisions related to the composition of debt, ordinary shares, and preferred shares that must be used by the company. Managers must be able to collect funds both sourced from within and outside the company effectively and efficiently. Funding decisions must be able to minimize the cost of capital that must be borne by the company (Prabansari and Kusuma, 2005). When managers use debt, capital costs will arise as much as the interest costs charged by creditors, whereas if managers use internal funds there will be opportunity costs. Funding decisions made inaccurately will result in fixed costs, namely high capital costs and subsequently result in a low profitability of a company (Brigham and Houston, 2001).
The research is expected to make a theoretical contribution and add to the academic literature by examining the effect of earnings smoothing, investment, and return on assets on the earnings aggressiveness. This research is expected to be able to analyze whether a company's financial reporting has adopted IFRS completely. The practical contribution of this research is expected to increase understanding of earnings aggressiveness clearly. The measurement of earnings aggressiveness is expected to be used to analyze and make investment decisions for a company. The practical contribution for company management and analysts is through the results of this study can consider the calculation of the earnings aggressiveness of the right to control annual reports, so that companies can make investment decisions more optimally.
Weston and Copeland (2010) defines if seen from the perspective of the company (agent), the cost of equity capital is the real cost incurred by the company to obtain funds to fund the investment or operations of the company and to obtain funds and provide satisfaction to investors at a certain level of risk. The way the company finances its assets is shown in the financial composition that is on the right side of the balance sheet, namely the amount of long-term debt, issuance of preferred shares, ordinary shares and retained earnings.
Investments in the capital market are investments that have a high level of risk. Modern portfolio theory (Markowitz, 1952) concludes that investment risk can be reduced by the formation of an efficient portfolio, so that the risk is lower than the risk of each investment instrument that forms the portfolio.
Earnings aggressiveness is the output of accounting aggressiveness policies and is the best way used by management in manipulating earnings, especially by increasing the company's profits temporarily (Penman, 2003). One dimension that leads to profit opaqueness is income smoothing. In the research of Tucker and Zarowin (2006), income smoothing is an act of earnings management by reporting company earnings on an average basis over time.
If accounting income is artificially flattened, the company's profit figure means it failed to properly describe economic performance, thereby reducing the informativeness of earnings reports and leading to earnings opacity. The measurement of income smoothing uses a negative correlation between changes in the discretionary accrual proxy and changes in pre- discretionary income. The greater negative correlation shows the greater income smoothing.
The more flat profit (the smaller negative correlation), the more informative the profit, and gives a positive signal to investors. Thus, income smoothing is an attempt by the company's management to reduce abnormal earnings variations by the range that is possible based on good accounting and management principles (Li and Richie, 2016).
The purpose of this study is to test and analyze (1) whether income smoothing influences earnings aggressiveness; (2) whether investment opportunities affect the profit aggressiveness; (3) whether the level of investment opportunity influences the earnings aggressiveness. Research contributions consist of theoretical contributions, practical contributions and policy contributions. This study confirms whether the aggressiveness of earnings can be explained by various factors. Theoretical contribution of research is significant for science, namely the effect of earnings smoothing, investment, and return on assets on the earnings aggressiveness.
The practical contribution of research is expected to increase understanding of earnings aggressiveness that previously led to unclear understanding. The measurement of earnings aggressiveness is expected to be used to analyze and make investment decisions for a company. Practical contribution for the company is through the results of this study can consider the calculation of profit aggressiveness that is appropriate to control the company's performance and annual reports and to pay more attention to the calculation of the composition of capital costs (costs in the annual report produced so the company can make investment decisions more optimally).
The contribution of the policy is by comprehensively testing the profit aggressiveness calculation, it is expected that the results of this study can be input for the Financial Services Authority (OJK). in compiling regulations in relation to overcoming the aggressiveness of earnings in presenting financial statements for listed companies listed on the stock exchange. For example, valuation regulations to make estimates needed under conditions of uncertainty, so that assets or profits are not over-recorded, liabilities and costs are not under-recorded. The results of this study are also expected to provide input for OJK to make policies related to comprehensive reports using earnings aggressiveness. The results of this
study are also expected to be input for the standard drafting council to add importance to the prevention of excessive profit aggressiveness contained in the financial statements.
This study examines companies that produce goods and services (manufacturing) with a research period of five years, from 2013-2017. Manufacturing companies listed on the stock exchange chosen as research samples are domiciled in the country of Indonesia. The country of Indonesia is used as a research sample because it is the domicile of researchers and the GDP (Gross Domestic Product) per capita is quite high in the thousands of US $ Dollars in Southeast Asia in 2017. Capital markets in Indonesia are quite active as a means of business funding (World Development Indicators).
The significance of this study is to examine the effect of income smoothing, investment opportunities, and the rate of return on assets on earnings aggressiveness. The aim is to analyze whether a company has considered whether there are indications of earnings aggressiveness in presenting financial reporting based on IFRS and to ascertain whether the financial statements are neutral and reasonable. This study examines the effect of income smoothing, investment opportunities, and the rate of return on assets to earnings aggressiveness.
LITERATURE REVIEW AND DEVELOPMENT OF HYPOTHESES
Agency Theory
The grand theory in this research is agency theory. Agency theory (Jensen and Meckling, 1976) assumes that every individual involved in a contract aims to maximize their respective interests. If the individual acts individually to maximize their interests, conflict will arise. So, each individual who entered into the contract aims to accommodate the interests of various parties, because they realize that the interests will be fulfilled if the common goals are also fulfilled.
Earnings Smoothing On Earnings Aggressiveness
Earnings smoothing is the action of management that reports earnings smoothly all the time.
Even with accounting earnings that are artificial (artificial smooth), the earnings figures fail to describe actual economic performance, so the informativeness of earnings reports decreases and causes earnings obscurity (Francis et al., 2004). Bhattacharya et al. (2003) defines income smoothing if it does not reflect the economic value of the company which will actually cause the cost of capital to increase. The greater correlation number indicates the greater income smoothing, resulting in greater profit opacity. In the research of Khaddaf et al. (2014) also investigates income smoothing. Income smoothing has a significant effect on stock returns through trading volume activities. Furthermore, management actions that lead to income smoothing can be detected through the accrual component (Dechow et al., 1995; Bhattacharya et al., 2003). Penman (2003) concluded that the higher the current operating income (current operating income) manipulated by management, the lower the rate of return on net operating assets (RNOA) in the coming period.
Sunarto et al. (2016) concluded that the aggressiveness of earnings affects investors in making decisions. Mendes-Da-Silva et al. (2014) uses the least-squares regression equation. The results of his research are that on average companies that are more aggressive show higher capital costs and are supported by a lack of research on how to estimate capital costs and their relationship with disclosure through company websites, especially in terms of considerations for developing countries, for example Brazil. Sunarto (2010) and Bhattacharya et al., (2003) conclude that earnings aggressiveness will lead to earnings blurring. The earnings statements presented in the company's financial statements
lead to more recorded profits so that accounting earnings do not reflect the economic performance of a company.
In discretionary accrual policy, if it provides earnings informativeness, then the policy increases earnings quality (Dechow and Dichev, 2002). On the other hand, if the policy does not reflect actual economic profit, then it creates profit opaqueness (Bhattacharya et al., 2003). Based on this discussion, the researcher proposes the following hypothesis:
H1: Earnings smoothing has a positive effect on earnings aggressiveness.
Investment Opportunities On Earnings Aggressiveness
According to Myers (1977), investment opportunity is a view of the value of a company as a combination of assets owned by investment choices in the future. Investment opportunities are measured by market value divided by book value in equity ratios. Titman et al. (2004) states that an investor needs to pay attention to the existence of the company's capital investment costs and how to target each decision-making. The research of Adam and Goyal (2003) states that investment opportunities play an important role in corporate finance in relation to achieving company goals. Earnings aggressiveness ultimately affects the decline in earnings quality (Altamuro et al., 2005). Kothari (2001) states that if a company performs accounting aggressiveness, then the book value of assets in the current period and profits will be higher, but for earnings predictions to be low and the cost of capital will increase.
Based on research by Ball et al. (2000) concluded that the opposite of earnings aggressiveness is accounting conservatism, where accounting conservatism means that a company recognizes losses faster and slower to recognize profits, appearing in common law countries to improve information asymmetry. They continue to argue that accounting conservatism is related to accounting transparency which implies that earnings aggressiveness has a positive effect on earnings opacity. Based on this discussion, the researcher proposes the following hypothesis:
H2: Investment opportunities has a negative effect on earnings aggressiveness.
Return on Assets On Earnings Aggressiveness
The rate of return on assets is one form of profitability ratio. The rate of return of assets to measure a company's ability to generate profits by using the total assets available and after capital costs (costs used to fund assets) are excluded from financial analysis (Ahmed et al., 2002). The rate of return on assets is measured by net income divided by the total assets of the company (Nikoomaram et al., 2011). Surifah (2015) aims to determine the effect of earnings management on the cost of equity capital. This study took a sample of manufacturing companies listed on the Indonesia Stock Exchange during 2011-2013. The data was obtained by using purposive sampling technique and using multiple regression analysis methods. This study uses the rate of return on assets as a control variable that affects the cost of equity capital. Frank et al. (2004) found that some companies tend to report aggressively for the purpose of financial and tax reporting, while there are other companies that tend to report conservatively for financial and tax reporting purposes. The results showed that companies involved in aggressive financial reporting were also involved in aggressive tax reporting. Based on this discussion, the researcher proposes the following hypothesis:
H3: The rate of return on assets has a negative effect on earnings aggressiveness.
RESEARCH METHODS
Research design
Based on the problems in this study, the type of research used is hypothesis testing for the presence or absence of earnings opacity (proxied by earnings aggressiveness), information asymmetry and earnings informativeness of capital costs with caution (prudence) as a
moderating variable in manufacturing companies in Indonesia and the Philippines . Testing the hypothesis is causal. The time period used in this study was five years from the 2013- 2017 period. The setting environment is the real environment. The unit of analysis used in this study uses the financial statements of manufacturing companies in Indonesia and the Philippines which have been audited and listed on the stock exchange and have company websites, stock exchange websites of each country and other supporting websites.
Variables and Measurements Dependent Variable
The calculation for earnings aggressiveness (Bhattacharya et al., 2003) is measured in the following stages.
Stage 1:
...(1) Explanation:
Y = Scala of accrual company k year t (EBITDA / TAkt-1)
ΔCAkt = Change in total current assets for the company k (CAkt – CAkt-1) ΔCLkt = Change in total current liabilities for the company k (CLkt – CLkt-1) ΔCASHkt = Change in cash for the company k (Cashkt – Cashkt-1)
ΔSTDkt = Change in short term debt for the company k (STDkt – STDkt-1)
DEPkt = Depreciation expense and amortization expense for the company k year t ΔTPkt = Change in tax payable (TPkt – TPkt-1)
TAkt-1 = Total asset company k year t-1
EBITDA = Earnings before interest, tax, depreciation, and amortization Stage 2:
The error value obtained is the earnings aggressiveness. The error value is absoluted first.
Independent Variable
Independent variables that will be tested to determine the relationship with the dependent variable in this study are as follows:
Earnings Smoothing
Earnings smoothing is measured by a calculation method based on the research of Perotti and Wagenhofer (2014). The measurement consists of several stages as follows:
Stage 1:
Calculations xit for earnings in each company for period of five years (xit, xit-1, xit-2, xit-3, xit-4)
...(2) Stage 2:
Calculations xit for cash flow from operation in each company for period of five years (xit, xit- 1, xit-2, xit-3, xit-4)
...(3) Stage 3:
The results of calculations in equation (2) and equation (3) are entered into the formula for variance in equation (4), respectively.
Y = α + β1 ΔCAkt + β2 ΔCLkt + β3 ΔCashkt + β4 ΔSTDkt + β5 ΔDEPkt + β6 ΔTPkt + ε TAkt-1 TAkt-1 TAkt-1 TAkt-1 TAkt-1 TAkt-1
xit = EARNt Asett-1
xit = CFOt Asett-1
...(4) Stage 4:
Calculations on the variance formula in equation (4) are then calculated in the form of standard deviations in equation (5)
...(5) Stage 5:
The results of calculations in equations (2) through equation (5) are included in the calculation of earnings smoothing in equation (6) as follows:
..………..………....……...(6) Explanation:
PL = Earnings smoothing σ = Standar Deviation
CFO = Cash Flow Operating
EARN = Earnings (Net Income Before Extraordinary Items) Asett-1 = Total Asset
Francis et al. (2004) measure this earnings smoothing from the ratio between earnings variability and cash flow variability. This measurement is based on the argument that the profit attribute is derived from management's judgment using its private information regarding future earnings to "flatten" the fluctuations that occur. So the income statement will be more representative and more useful. This smoothing measurement model is also used by Ecker et al. (2006).
Investment opportunity (INVEST)
Sustainability of a company is determined by financial performance that is perceived by firm value. Investment opportunity set affects firm value (Nanda et al, 2018). Investment opportunities are calculated from market value divided by book value of equity (Myers, 1977). Keown et al. (2010) states that when a company's investment opportunity rises, the dividend payout ratio must decrease.
Return on assets (ROA)
The rate of return on assets is measured by net income divided by total company assets (Nikoomaram et al., 2011; Francis et al., 2008; Ahmed et al., 2002). According to Harahap (2010), Return On Assets (ROA) describes the asset turnover measured from sales. The greater this ratio, the better and this means that assets can more quickly get returns and achieve profits.
Data Analysis Method
Testing the Relationship of Independent Variables and Dependent Variables
This research examines descriptive statistics of each variable and its correlation with other variables, Pearson correlation and multiple regression. Testing of research with Eviews 9.
Analysis of Descriptive Statistical
The analysis is used to determine the characteristics of the data, namely the mean, median and standard deviation. Descriptive statistics relate to data collection and presentation of
σ
2= n ∑
ni=1x
i2- ( ∑
ni=1x
i)
2n (n-1)
σ =
n ∑ni=1 xi2 - ( ∑ni=1 xi )2 n (n-1)PL = σ (EARN /Aset
t-1) / σ (CFO /Aset
t-1)
summary data results. In addition, a normality test and a classic assumption test (multicollinearity, heteroscedasticity, and autocorrelation) were also performed. Outlier data is data that deviates considerably from other data in a data set. This outlier data makes the analysis of a series of data biased or has the potential to disrupt the central tendency of research data (Santosa and Hidayat, 2014). The term outlier is also often associated with extreme values, both large and small extremes. The detection of outlier data can be done by first determining the mean and standard deviation of each variable, then determining standardize and absolute standardize. Values range from -3 to +3 due to the large amount of data. If the test results show the existence of outlier data, then what can be done is to remove or eliminate (trimming) the observational data because if it is not removed it gives effect after testing.
Normality Test
Normality test aims to determine the distribution of data in variables that will be used in research. Data that is feasible to use is data that has a normal distribution. The normality test also aims to test whether in the regression model, confounding or residual variables have a normal distribution. Data normality test is performed before the data is processed based on parametric statistical models. Testing residual normality in this study using the Jarque-Bera (JB) test with the Eviews program. JB Test is a normality test for large samples (Ghozali and Ratmono, 2013). For treatment if an abnormally distributed model, the researchers used the Lisrel 8.80 software.
Classic Assumption Test
Before conducting hypothesis testing, the data obtained in this study were tested first in order to meet basic assumptions. Tests carried out include:
Multicollinearity Test
This test is used to determine whether there are independent variables that have similarities between the independent variables in a model. The similarity between independent variables can cause a very strong correlation. Multicollinearity test also aims to avoid the habit of the decision-making process regarding the effect of partial test of independent variables on the dependent variable. The method for detecting multicollinearity is to test the Variance Inflation Factor (VIF). VIF is produced between 1-10, so there is no multicollinearity (Gujarati, 2010). The formula is as follows:
.………...………...………(7) According to Gujarati (2010), if the VIF is greater than 10, then among the independent variables multicollinearity is suspected. The regression model becomes a model that is free from multicollinearity if the VIF value is less than 10. In this study also will use a correlation matrix as an addition to analyze multicollinearity. According to Ghozali and Ratmono (2013), if there is a correlation between high independent variables above 0.90 then multicollinearity is suspected.
Heteroscedasticity Test
Heteroscedasticity test examines the difference in residual variance from one observation period to another (Ghozali and Ratmono, 2013). In testing the presence or absence of heteroscedasticity, this study uses a statistical test method (formal test), namely Glejser in the Eviews version 9 program.
Autocorrelation Test
Testing the next classic linear regression model is the autocorrelation test. To test the autocorrelation in this study the Durbin-Watson test is used which requires an intercept or constant in the regression model (Ghozali and Ratmono, 2013). The way to do the Durbin-
VIF = 1
tolerance
Watson test is to estimate the regression in Eviews first, then the output presents the DW value.
Regression Analysis
In this study, the analysis test was conducted with the Eviews version 9 program using panel data regression which is a combination of time series data due to year order and cross section due to the large number of companies. The model test results in this study are said to be significant if the probability is <1%, <5%, and <10%. The results of the classic assumption test are presented in Table 1.
Table 1: Classic Assumption Test No. Classic Assumption
Test
Statistical Test Method Results 1. Multicollinearity Test free from multicollinearity
2. Heteroscedasticity Test Glejser Test no heteroscedasticity 3. Autocorrelation Test Durbin-Watson no autocorrelation
Source: Data processed, regression output
RESULTS AND DISCUSSION
1. Descriptive Statistics and Correlation Matrices
In this study conducted a descriptive statistical analysis with the aim to determine the distribution of data in the form of central tendencies and data dispersion. The results of the descriptive statistical analysis of the research variables are presented in Table 2.
Table 2: Descriptive Statistical – Research Variable
N Minimum Maximum Mean Std.
Deviation Dependent Variable :
AGGRESS 500 -0.26480 0.83970 0.09571 0.13332 Independent Variable :
SMOOTH 500 -1.96780 3.20440 0.93285 1.08995 INVEST 500 -2.14210 1.85930 -0.23949 0.64116
ROA 500 -0.61280 0.71830 0.04909 0.15484
Note: This table represents descriptive statistics of each research variable. The purpose of this table is to provide an overview of the conditions of central tendency and the dispersion of data used in estimating research models. The dependent variable is AGGRESS. The independent variables are SMOOTH, INVEST, ROA
Source: Data processed, regression output
Based on the data in Table 2, the earnings aggressiveness variable (AGGRESS) has the lowest value of -0.26480 and the highest value of 0.83970. Aggressiveness in positive earnings shows financial statements are influenced by earnings aggressiveness.
Companies must pay attention to the standards of propriety in the presentation of financial statements. Profit aggressiveness variable (AGGRESS) has a standard deviation value greater than the average value. This shows that the profit aggressiveness variable (AGGRESS) of the sample companies has quite a high variation of these variables. Earnings smoothing (SMOOTH) has a relatively small mean value compared to the standard deviation
value. This shows that the variability of income company sample smoothing is quite high.
Most of the sample companies make uneven and fluctuating income smoothing. Investment opportunity has a relatively small mean value compared to the standard deviation value. The investment opportunity variable has a negative average and there is a high enough variation of the variable for the sample company. The variable return on assets has a relatively small average value compared to the standard deviation value. This means that there is a high enough variation of these variables for the sample company. The variable return on assets also has a positive average value. This indicates that on average, the sample companies have return on assets in every financial report presentation.
Hypothesis Test Results
The classic assumption test of this research model shows that the model does not experience multicollinearity, heteroscedasticity and autocorrelation problems.
Research Hypothesis Testing Results
The test of this research model is a regression test conducted to see the effect of income smoothing, investment opportunities, and the rate of return of assets on earnings aggressiveness. The results of testing the first model research hypothesis are presented in Table 3.
Table 3: Model Testing Results
AGGRESSit = β0 + β1SMOOTHit + β2INVESTit + β3ROAit + εit
Variable Prediction Coefficient P-Value Statistik Collinearity Tolerance VIF
Constanta 0.0645 0.0000 -- --
SMOOTH + 0.0398 0.0000***) 0.9337 1.0711
INVEST - -0.0119 0.1681 0.9523 1.0501
ROA - -0.1796 0.0000***) 0.9337 1.0710
Normality Test 0.9902 Durbin-Watson Stat 1.9515 Glejser Test 0.2621 Adjusted R2 0.1763 Prob (F-Statistik) 0.0000***) Total of Observation 500
*** Significant at the level of 1%; ** Significant at the level of 5%; * Significant at the level of 10%.
Note: This table represents the descriptive statistics of each research variable. The purpose of this table is to provide an overview of the conditions of central tendency and dispersion of the data used in estimating the research model. The dependent variable is AGGRESS. Independent variables are SMOOTH, INVEST, and ROA
Source: Data processed, regression output
The first hypothesis (H1) states that earnings smoothing has a positive effect on earnings aggressiveness. Statistical test results show the value of earnings smoothing coefficient of 0.0398 and sig. 0.0000. This means that income smoothing has a significant positive effect on earnings aggressiveness. In the second hypothesis (H2) it is stated that investment opportunities has a negative effect on earnings aggressiveness. Statistical test results show the value of the regression coefficient on the investment opportunity variable of -0.0119 and not significant. This means that investment opportunities do not affect the earnings aggressiveness made by the company. The third hypothesis (H3) states that the rate of return on assets negatively affects the aggressiveness of earnings. Statistical test results show the value of the regression coefficient on the variable return on assets of -0.1796 and significant at the level of 1% (sig. 0.0000). This means that the rate of return on assets has
a significant negative effect on earnings aggressiveness. Based on the results of these tests, the discussion of the results of model testing is as follows. Hypothesis 1 (H1) which is formulated that income smoothing has a positive effect on earnings aggressiveness is supported by research results. These results indicate that management actions that smooth the company's profits are responded by investors in making decisions. In other words, the more aggressive the company, the higher the income smoothing. The results of this study support the findings of Bhattacharya et al. (2003) that earnings aggressiveness will lead to earnings blur. Companies that report earnings policies very aggressively tend to be at high risk, due to excessive income smoothing. This finding is also in accordance with agency theory where investors realize that management usually makes decisions that are not in the best interests of investors. The existence of high profit aggressiveness, companies tend to be at risk because of earnings smoothing.
Hypothesis 2 (H2) which is formulated that investment opportunities negatively affect earnings aggressiveness is not supported by research results. Based my argument, these results indicate that companies that implement policies to make investment opportunities will not affect the earnings aggressiveness made in preparing financial statements.
Hypothesis 3 (H3) which is formulated that the rate of return on assets negatively affects the earnings aggressiveness by research results. The argument underlying this result is that conceptually a company that has a rate of return on assets will not affect the aggressiveness of profits that occur in a company. This finding is in accordance with agency theory where the management tends to present higher profits than actual earnings, thus leading to earnings aggressiveness.
Overall, the output of the first model shows the adjusted R2 value of 0.1763, which means that the variation of the independent variable is able to explain 17.63% of the variable Y. So the regression model is good, while the remaining 82.37% is explained by other variables not examined.
CONCLUSIONS
Income smoothing can increase earnings aggressiveness that affect company performance and increase company risk. Investment opportunities do not have a significant effect on earnings aggressiveness. This indicates that investment opportunities that rise or fall will not affect the appearance of profit aggressiveness. The rate of return on assets will be able to control the aggressiveness of earnings in a company.
Research has limitations that need to be addressed so that the interpretation of research results is carried out carefully by considering existing limitations. In addition, the limitations of existing research are useful to be considered for future research. The limitations referred to are (1) The results of this study cannot be generalized to all countries. The results of the study only apply to publicly traded companies in the country of Indonesia; (2) In the research sample of companies that go public, there are components such as depreciation and amortization costs that are not separated from the company cost component, so researchers must check back to the notes on financial statements and annual reports; (3) In the company's financial statements there are those whose profits are negative and those whose book value of equity is negative.
This research gives theoretical implications that earnings smoothing has a significant positive effect on the earnings aggressiveness. Return on asset has a significant negative effect on earnings aggressiveness. The results of this study also have managerial implications for regulators. The quality of a company's earnings reporting will be high or low
is not only determined by accounting policies or company internal factors. The role of the regulator is needed for supervision of earnings reporting. The formulation of regulations in a country's Financial Accounting Standards is applied as a form of limiting the flexibility of accounting policies and to narrow the opportunist attitudes of the company's management.
Suggestions for further research are as follows (1) Extending the sample of companies by industry category. In this study using manufacturing companies only. Future studies can use company samples for all industry categories, except the financial industry because of their different characteristics; (2) Adding more research samples from ASEAN countries. This research is limited to Indonesia. Further research can add to other ASEAN countries, namely Singapore, the Philippines, Malaysia and Thailand, which can be used as research samples.
By using other ASEAN country samples, further research is expected to be more extensive and comprehensive.
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